Least squares SVM approach for abnormal brain detection in MRI using multiresolution analysis

Developing automatic and accurate computer-aided diagnosis (CAD) systems for detecting brain disease in magnetic resonance imaging (MRI) are of great importance in recent years. These systems help the radiologists in accurate interpretation of brain MR images and also substantially reduce the time needed for it. In this paper, a new system for abnormal brain detection is presented. The proposed method employs a multiresolution approach (discrete wavelet transform) to extract features from the MR images. Kernel principal component analysis (KPCA) is harnessed to reduce the dimension of the features, with the goal of obtaining the discriminant features. Subsequently, a new version of support vector machine (SVM) with low computational cost, called least squares SVM (LS-SVM) is utilized to classify brain MR images as normal or abnormal. The proposed scheme is validated on a dataset of 90 images (18 normal and 72 abnormal). A 6-fold stratified cross-validation procedure is implemented and the results of the experiments indicate that the proposed scheme outperforms other competent schemes in terms of classification accuracy with relatively small number of features.